{"id":"W4386474156","doi":"10.1109/lcn58197.2023.10223377","title":"Towards Energy Efficiency in RAN Network Slicing","year":2023,"lang":"en","type":"article","venue":"","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Nature; CHIST-ERA; Agence Nationale de la Recherche","keywords":"Slicing; Quality of service; Computer science; Energy consumption; Efficient energy use; Computer network; Base station; Energy (signal processing); Service (business); Work (physics); Distributed computing; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00227018,0.001208285,0.001101986,0.0006089828,0.0005980575,0.001714812,0.001430552,0.000874539,0.002527209],"category_scores_gemma":[0.00485491,0.0004357031,0.000606573,0.0008176698,0.0007291712,0.001981111,0.00153514,0.001196395,0.0004510992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016856,"about_ca_system_score_gemma":0.001383192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004018072,"about_ca_topic_score_gemma":0.005551778,"domain_scores_codex":[0.998769,0.0005461839,0.00004769994,0.0002047286,0.0002198221,0.0002125639],"domain_scores_gemma":[0.9983014,0.0008949375,0.0001518526,0.0003041273,0.0002525422,0.00009509999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001424829,0.00005596977,0.0009116104,0.00008889326,0.00005745646,0.00008964293,0.00008171178,0.9208025,0.003530764,0.02065249,0.001595966,0.0519904],"study_design_scores_gemma":[0.000009460628,0.00003055612,0.000162731,0.00001329364,0.00001302279,0.00003542332,0.0000428491,0.9843398,0.001122034,0.01290374,0.001322081,0.000005064519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03978787,0.001351336,0.9516016,0.0004826468,0.00007858648,0.00007475305,0.0001149123,0.0005162035,0.005992135],"genre_scores_gemma":[0.7444413,0.0007950185,0.251176,0.0002492763,0.00006426192,0.00008465425,0.0002428659,0.0002110379,0.002735536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004018072,"threshold_uncertainty_score":0.01200598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587357276177394,"score_gpt":0.2342043666366224,"score_spread":0.2183307938748484,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}